Quantile Mapping Bias Correction on Rossby Centre Regional Climate Models for Precipitation Analysis over Kenya, East Africa

Abstract

This study uses the quantile mapping bias correction (QMBC) method to correct the bias in five regional climate models (RCMs) from the latest output of the Rossby Center Climate Regional Model (RCA4) over Kenya. The outputs were validated using various scalar metrics such as root-mean-square difference (RMSD), mean absolute error (MAE), and mean bias. The study found that the QMBC algorithm demonstrates varying performance among the models in the study domain. The results show that most of the models exhibit reasonable improvement after corrections at seasonal and annual timescales. Specifically, the European Community Earth-System (EC-EARTH) and Commonwealth Scientific and Industrial Research Organization (CSIRO) models depict remarkable improvement as compared to other models. On the contrary, the Institute Pierre Simon Laplace Model CM5A-MR (IPSL-CM5A-MR) model shows little improvement across the rainfall seasons (i.e., March–May (MAM) and October–December (OND)). The projections forced with bias-corrected historical simulations tallied observed values demonstrate satisfactory simulations as compared to the uncorrected RCMs output models. This study has demonstrated that using QMBC on outputs from RCA4 is an important intermediate step to improve climate data before performing any regional impact analysis. The corrected models may be used in projections of drought and flood extreme events over the study area.

Future Changes in Precipitation Extremes over East Africa Based on CMIP6 Models

“…The two main projections utilized are drawn from Tier 1 ScenarioMIP: SSP2-4.5 and SSP5-8.5. Following the recommendations of previous studies (e.g., [57,[81][82][83][84][85]), this employs MME of CMIP6 for projection of extreme events over the study area. Many studies have remarked on the robustness of MME as compared to individual models due to cancellation of intermodel biases [59].…”

Observed and Future Precipitation and Evapotranspiration in Water Management Zones of Uganda: CMIP6 Projections

“…Existing studies have pointed out the simulated precipitation over Uganda and the Greater Horn of Africa depends on the boundary layers, the model horizontal resolution, and the parameterization schemes [51][52][53][54]. In fact, in a recent study over East Africa [55] established that the biases in GCMs are not fully corrected to resemble observed patterns, despite the implementation of various robust statistical methods such as quantile approach. Even Regional Climate Models (RCMs) which are of spatially finer resolution than the GCMs were found to be largely biased in reproducing the East African climatology [56].…”

Comparison and Bias-Correction of Satellite-Derived Precipitation Datasets at Local Level in Northern Kenya

“…The KMD dataset was therefore corrected using the Quantile Mapping bias correction algorithm technique, which has been widely used for correction of precipitation datasets [57][58][59] and has demonstrated high performances in arid and semi-arid areas [33,60]. In particular, the work of Ringard et al demonstrated its usefulness for satellite-derived datasets correction in scarce observed data contexts [61].…”

“…The relationship between large-scale weather systems and local climate varies from region to region, making necessary to evaluate and correct them at local scale [23,24], but the scarcity of land surface observation is one of the greatest difficulties in assessing dataset performances [25]. Previous studies have tried to assess the performance of satellite-derived and model-derived datasets in East Africa [26][27][28][29][30][31][32], in particular in Kenya [33][34][35], in order to address the lack of data from land-based meteorological stations. However, these studies have a more regional perspective rather than a local focus, and further investigation on their use at local scale is needed.…”